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Method and system for predicting synonym tree for Chinese and English word pairs

A Chinese-English, word technology, applied in the field of Chinese-English word pair prediction semantic tree

Active Publication Date: 2021-03-30
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present invention provides a method and system for predicting sememe trees for Chinese and English word pairs, so as to solve the defect in the prior art that the category sememes of word pairs can only be marked manually

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  • Method and system for predicting synonym tree for Chinese and English word pairs
  • Method and system for predicting synonym tree for Chinese and English word pairs
  • Method and system for predicting synonym tree for Chinese and English word pairs

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Embodiment Construction

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0051] figure 1 is a schematic diagram of the sememe tree provided by the embodiment of the present invention, such as figure 1 As shown, the following concepts are included:

[0052] Word meaning (sense): the meaning or meaning of a word, it is people's general understanding of the thing, phenomenon, and relationship called by a word, and sen r...

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Abstract

The embodiment of the invention provides a method and a system for predicting a synonym tree for Chinese and English word pairs. The method comprises the following steps: acquiring a word pair to be predicted and a category synonym corresponding to the word pair to be predicted; and based on a known predefined source set, a semantic relationship set and the category semantic source corresponding to the to-be-predicted word, predicting the to-be-predicted word pair by adopting a predefined source tree generation algorithm to generate a semantic source tree. According to the embodiment of the invention, through the known synonym knowledge base, the category synonym information of the word pairs is given, and the synonym tree is predicted for the given word pairs, so that the automatic synonym tree prediction is realized, and compared with manual synonym tree labeling that a lot of time and cost need to be consumed, the method and the system have the characteristics of higher efficiency and higher accuracy.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a method and system for predicting sememe trees for Chinese and English word pairs. Background technique [0002] Sentences are composed of words, and different words have similarities and differences. HowNet is a widely used artificial annotation database, which is used to describe the semantics of different words. It marks words as a structure composed of a series of sememes, and sememes are smaller and indivisible semantic sets than words. It has a more basic meaning than vocabulary. HowNet and its labeled sememe information can be used in natural language processing tasks such as word disambiguation, sentiment analysis, cross-language word similarity, and word vector generation. [0003] Although sememes play an important role in natural language analysis and processing, manual labeling of sememes is a time-consuming and laborious task, and there are ine...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/35G06F40/30
CPCG06F16/367G06F16/353G06F40/30
Inventor 李涓子刘宝巨侯磊张鹏唐杰许斌
Owner TSINGHUA UNIV
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